Identification and Verification of Translational Biomarkers for Patient Stratification and Precision Medicine

Transcription

1 Biomarkers for Patients Stratification and Precision Medicine -- Cancer and Beyond AAPS National Biotechnology Conference, Boston on May 16-18, 2016 Identification and Verification of Translational Biomarkers for Patient Stratification and Precision Medicine Bin Li, PhD, Associate Director Translational Medicine Takeda Pharmaceuticals International Co. May 18, 2016

2 Disclaimer Any views or opinions presented here are solely those of the author and do not necessarily represent those of the company.

3 Outline Design and implement a predictive modeling framework Method evaluation Using SOC drugs to evaluate the predictive modeling approach Build Erlotinib and Sorafenib sensitivity models using cell line data Tested the models using real patients output (PFS from BATTLE trials) Predict potential drug target cancer indications Method comparison on NCI-DREAM challenge A fair comparison on predictive modeling methods, used by 47 teams on 22 drugs

4 What we want to achieve on the predictive models Goal: To discover patient selection biomarkers using preclinical data, e.g. using cell line viability screens We want to be testing hypothesis in Ph II and using Ph III clinical trials to select the right patients for treatment What we want to gain from building the predictive models Build a predictive model on cell line data, which can be used for patients Model derived signature genes reflects a drug s MOA Each drug s predictive model should be drug specific Using the predictive model for cancer indication selection

5 Design and development of a PLSR based predictive modeling framework Learned from FDA organized MAQC_II observations Nat. Biotech. 2010, 28, academic and industry teams participated Most of the state-of-art computational approaches were applied We decided to build our modeling framework based partial least squares regression (PLSR) method Technique considerations on choosing PLSR method: PLSR is similar to PCA, while it could be more effective than PCA since it targets best X-Y relationship (PCA captures key variance in X only) PLSR is well known as a dimension reduction tool (ideal for genomic data) PLSR was designed to address multicollinearity issue Learned from CCLE and Sanger observations Nature, 2012, 483, 603; Nature, 2012, 483, 570 Both teams adopted Elastic Net (EN) method for biomarker discovery, while EN approach is very similar to PLSR (EN seeks max coefficient vs PLSR seeks max covariance)

6 A specially designed modeling framework to capture consensus information from the dataset (A) Input data: GEP & IC50 (B) Panel cell lines Training a model on cell line data Data reduction Feature selection Model training using a specially designed splitting strategy Obtain a core model (C) 42% random.training 28% random.validation 30% balance.validation (10-fold cross validation) Random.validation Sub-testing AUC vs. cor Correlation Correlation Balance.validation Sep-testing AUC vs. cor Get a pathway based core model (D) AUC AUC Testing the model on patients data Test model Bin Li, Hyunjin Shin, et al. PLoS ONE 2015, 10(6): e

7 Pathway-based signature genes network reconstruction

8 Outline Design and implement a predictive modeling framework Method evaluation Using SOC drugs to evaluate the predictive modeling approach Build Erlotinib and Sorafenib sensitivity models using cell line data Tested the models using real patients output (PFS from BATTLE trials) Predict potential drug target cancer indications Method comparison on NCI-DREAM challenge A fair comparison on predictive modeling methods, used by 47 teams on 22 drugs

9 Trained the Erlotinib PLSR model on cell line data: Built a good model with 77-91% accuracy Ricerca Oncopanel cell based assay: Re-predicting Ricerca log2(ic50) Prostate Pancreas Lung Liver Skin Kidney Head_and_Neck not_classified Soft_Tissue Haematopoietic_a nd_lymphoid Bladder Breast CNS Colon Colon_GI Endocrine Female_GU_Cervi x Female_GU_other Ricerca Screen: cell lines - varying histological origin -10 point dose titration => IC50s - Baseline Microarray data Accuracy estimation: Upper boundary: 91% Lower boundary: 77%

10 Overall work flow on model building and testing: on Erlotinib case study Training a model on cell line data Testing the model on patients data Input data: GEP & IC50 Data reduction Feature selection Model training using a specially designed splitting strategy Obtain a core model Get a pathway based core model Test model probesets 3787 probesets 485 genes (full model) 187 core genes (core model) 51 genes (final pathway model)

11 Signature genes reflect known MOA of Erlotinib and Sorafenib (A) Erlotinib (B) Sorafenib

12 Test the Erlotinib model using BATTLE clinic trial: background Trained a PLSR model using Erlotinib Ricerca screen data (IC50s) On Affymetrix U133plus; 240 cell lines on multiple cancer types Using the model to predict patients survival (PFS) in BATTLE clinic trial On Affymetrix Hu Gene 1.0 ST; 25 Erlotinib patients on NSCLC

13 Cell line derived drug sensitivity models were able to predict patients responses and are drug specific (A) E_model pred E_PFS (B) S_model pred S_PFS Proportion of Cases P = 0.09 HR = 0.43 Proportion of Cases P = HR = Monthes from Start of Therapy Monthes from Start of Therapy (C) E_model pred S_PFS (D) S_model pred E_PFS Proportion of Cases P = 0.32 HR = 1.87 Proportion of Cases P = 0.54 HR = Monthes from Start of Therapy Monthes from Start of Therapy E: Erlotinib; S: Sorafenib; red: marker pos; green: marker neg

14 Using the predictive models for drug target cancer indication selection: build a translational medicine data warehouse To provide a large, well organized, and integrated dataset Compound screen data, public data, and clinic outputs Standardize vocabulary and Normalize experimental results Use standard terminology for the same concepts, e.g. gender, sex Global normalization on gene expression data Current data in Vahalla Takeda proprietary data: VELCADE clinical trials; cancer cell line screen data on multiple Takeda Oncology compounds Public data: > 2000 GEO studies, with ~ 350 GEO studies manually curated; TCGA data; CCLE and Sanger cancer cell line panel data A R-interface for computational scientists to build/test predictive models

15 Predicting Erlotinib or Sorafenib sensitive percentage of samples to select drug target cancer indications Cancer Type # of samples Pred. Erlotinib Sen. percentage Pred. Sorafenib Sen. percentage FDA approved Erlotinib or Sorafenib indications: Lung Cancer Liver Cancer Kidney Cancer Some interesting indications predicted to be sensitive to Erlotinib or Sorafenib: Head and Neck Cancer Bladder Cancer Haematopoietic Neoplasms Bone Cancer Breast Cancer Colorectal Cancer Pancreatic Cancer Erlotinib was approved in Lung cancer Sorafenib was approved in Live and Kidney cancers Erlotinib failed in one Kidney cancer PIII clinical trial Predicted additional Erlotinib sensitive cancer indications Predicted additional Sorafenib sensitive cancer indications Bin Li, Hyunjin Shin, et al. PLoS ONE 2015, 10(6): e

16 What we want to achieve on the predictive models Goal: To discover patient selection biomarkers using preclinical data, e.g. using cell line viability screens We want to be testing hypothesis in Ph II and using Ph III clinical trials to select the right patients for treatment What we want to gain from building the predictive models Build a predictive model on cell line data, which can be used for patients Model derived signature genes reflects a drug s MOA Each drug s predictive model should be drug specific Using the predictive model for cancer indication selection

17 Outline Design and implement a predictive modeling framework Method evaluation Using SOC drugs to evaluate the predictive modeling approach Build Erlotinib and Sorafenib sensitivity models using cell line data Tested the models using real patients output (PFS from BATTLE trials) Predict potential drug target cancer indications Method comparison on NCI-DREAM challenge A fair comparison on predictive modeling methods, used by 47 teams on 22 drugs

18 NCI-DREAM consortium testing drug sensitivity predictive models: 47 teams and 31 drugs The NCI-DREAM training, testing, and scoring (Nat. Biotech. 2014, 32, ): Official Results (Nat. Biotech) TMed comp bio team excluded 9 drugs with flat IC50 distributions: Follow-up analysis (internal)

19 TBOS team offically ranked No. 10 among 44 teams (while we only predicted 22 out of 31 drugs) For Official analysis on 31 drugs: TBOS team ranks No. 10 TBOS team teamfin Team_475 Team_690 Team_511 Team_680 Team_425 Team_603 TMM1_GEP Team_689 Team_691 Team_176 Team_418 Team_474 Team_545 For the 22 drugs we built predictive models: TBOS team ranks No. 3 on summarized scores and No. 1 on median scores

20 Conclusions: We designed and implemented a predictive modeling framework The predictive modeling approach was validated on Erlotinib/Sorafenib Cell line data derived models can predict patients response Model derived signature genes could reflect each drug s MOA Predictive models are drug specific The models were also able to predict potential drug target cancer indications Method comparison on NCI-DREAM challenge Our biomarker method was highly competitive among 47 teams on 22 drugs

21 Acknowledgements TMed project leaders Yuko Ishii Bill Trepicchio Steve Blakemore George Mulligan Andy Dorner TMed computational team Gene Shin Andrew Krueger Dave Merberg Lei Shen TMed assay team Sunita Badola Scott Verrow Elena Izmailova Thomas Reuters Olga Pustovalova Georgy Gulbekyan Marina Bessarabova